






'''simple docstring'''







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCAmelCase        =							TypeVar("T")




class   __SCREAMING_SNAKE_CASE       (Generic[T]       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__ = True	):






         """simple docstring"""


         a_						     =			{}  # dictionary of lists
         a_						     =			directed







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         if not self.directed:  # For undirected graphs
                # if both source vertex and destination vertex are both present in the
                # adjacency list, add destination vertex to source vertex list of adjacent
                # vertices and add source vertex to destination vertex list of adjacent
                # vertices.
                if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                       self.adj_list[source_vertex].append(UpperCamelCase__	)
                       self.adj_list[destination_vertex].append(UpperCamelCase__	)
                # if only source vertex is present in adjacency list, add destination vertex
                # to source vertex list of adjacent vertices, then create a new vertex with
                # destination vertex as key and assign a list containing the source vertex
                # as it's first adjacent vertex.
                elif source_vertex in self.adj_list:
                       self.adj_list[source_vertex].append(UpperCamelCase__	)
                       a_						     =			[source_vertex]
                # if only destination vertex is present in adjacency list, add source vertex
                # to destination vertex list of adjacent vertices, then create a new vertex
                # with source vertex as key and assign a list containing the source vertex
                # as it's first adjacent vertex.
                elif destination_vertex in self.adj_list:
                       self.adj_list[destination_vertex].append(UpperCamelCase__	)
                       a_						     =			[destination_vertex]
                # if both source vertex and destination vertex are not present in adjacency
                # list, create a new vertex with source vertex as key and assign a list
                # containing the destination vertex as it's first adjacent vertex also
                # create a new vertex with destination vertex as key and assign a list
                # containing the source vertex as it's first adjacent vertex.
                else:
                       a_						     =			[destination_vertex]
                       a_						     =			[source_vertex]
         else:  # For directed graphs
                # if both source vertex and destination vertex are present in adjacency
                # list, add destination vertex to source vertex list of adjacent vertices.
                if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                       self.adj_list[source_vertex].append(UpperCamelCase__	)
                # if only source vertex is present in adjacency list, add destination
                # vertex to source vertex list of adjacent vertices and create a new vertex
                # with destination vertex as key, which has no adjacent vertex
                elif source_vertex in self.adj_list:
                       self.adj_list[source_vertex].append(UpperCamelCase__	)
                       a_						     =			[]
                # if only destination vertex is present in adjacency list, create a new
                # vertex with source vertex as key and assign a list containing destination
                # vertex as first adjacent vertex
                elif destination_vertex in self.adj_list:
                       a_						     =			[destination_vertex]
                # if both source vertex and destination vertex are not present in adjacency
                # list, create a new vertex with source vertex as key and a list containing
                # destination vertex as it's first adjacent vertex. Then create a new vertex
                # with destination vertex as key, which has no adjacent vertex
                else:
                       a_						     =			[destination_vertex]
                       a_						     =			[]

         return self







  def __repr__(			self	):






         """simple docstring"""


         return pformat(self.adj_list	)












'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
__lowerCAmelCase        =							logging.getLogger(__name__)


def       __UpperCamelCase					(      lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			git.Repo(search_parent_directories=lowercase_   )
       a_						     =			{
           'repo_id': str(lowercase_   ),
           'repo_sha': str(repo.head.object.hexsha   ),
           'repo_branch': str(repo.active_branch   ),
       }

       with open(os.path.join(lowercase_   ,       'git_log.json'   )   ,       'w'   ) as f:
              json.dump(lowercase_   ,       lowercase_   ,       indent=4   )


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ):
       """simple docstring"""




       if params.n_gpu <= 0:
              a_						     =			0
              a_						     =			-1
              a_						     =			True
              a_						     =			False
              return

       assert torch.cuda.is_available()

       logger.info('Initializing GPUs'   )
       if params.n_gpu > 1:
              assert params.local_rank != -1

              a_						     =			int(os.environ['WORLD_SIZE']   )
              a_						     =			int(os.environ['N_GPU_NODE']   )
              a_						     =			int(os.environ['RANK']   )

              # number of nodes / node ID
              a_						     =			params.world_size // params.n_gpu_per_node
              a_						     =			params.global_rank // params.n_gpu_per_node
              a_						     =			True

              assert params.n_nodes == int(os.environ['N_NODES']   )
              assert params.node_id == int(os.environ['NODE_RANK']   )

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              a_						     =			1
              a_						     =			0
              a_						     =			0
              a_						     =			0
              a_						     =			1
              a_						     =			1
              a_						     =			False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       a_						     =			params.node_id == 0 and params.local_rank == 0
       a_						     =			params.n_nodes > 1

       # summary
       a_						     =			F'--- Global rank: {params.global_rank} - '
       logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes   )
       logger.info(PREFIX + 'Node ID        : %i' % params.node_id   )
       logger.info(PREFIX + 'Local rank     : %i' % params.local_rank   )
       logger.info(PREFIX + 'World size     : %i' % params.world_size   )
       logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node   )
       logger.info(PREFIX + 'Master         : %s' % str(params.is_master   )   )
       logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node   )   )
       logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu   )   )
       logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()   )

       # set GPU device
       torch.cuda.set_device(params.local_rank   )

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info('Initializing PyTorch distributed'   )
              torch.distributed.init_process_group(
                  init_method='env://'   ,       backend='nccl'   ,       )


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       np.random.seed(args.seed   )
       torch.manual_seed(args.seed   )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed   )




